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Outline

OUTLINE

The outline is keyed to the book “Mosty Harmelss Econometrics (MHE)” Princeton University Press 2008 table of contents.

Lecture 1: Making Regression Make Sense . . . Quickly (review of Chapter 3)

3 reasons to love
The CEF is all you need
The long and short of regression anatomy
Omitted variables bias

Lectures 2 and 3: Instrumental Variables in Action (highlights from Chapter 4)

Constant-effects models
    IV and omitted variables bias: estimating a “long regression” without controls
    Two-stage least squares (2SLS); 2SLS lingo and mistakes
    The Wald estimator, grouped data, and two-sample IV
    The bias of 2SLS (4.6.4)
Instrumental variables with heterogeneous potential outcomes
    Local average treatment effects; internal vs. external validity
    The compliers concept; identification of effects on the treated and ATE
    IV in randomized trials
Additional topics
    Average causal response in models with variable treatment intensity
    ACR examples
    Limited dependent variables and 2SLS: just do it! (4.6.3)

Lectures 4 and 5: Differences-in-Differences and Panel Data (Chapter 5)

DD basics
    The DD model
    Regression DD
    Examples
    DD assumptions and spec checks
    More examples
DD frontiers
    Synthetic Controls
    Changes-in-changes (time-permitting)

Lectures 6 and 7: Regression-Discontinuity Designs (Chapter 6)

Sharp RD
    Regression (parametric) RD
    Nonparametric RD
    Examples
Fuzzy RD
    Parametric and fuzzy
    Nonparametric fuzzy RD
    Examples
RD frontiers
    Bandwidth business

Lecture 8: Nonstandard Standard Error Issues (Chapter 8)

The bias of robust standard errors
    Why robust standard errors are biased
    A very simple example just to make the point
Clustering and serial correlation
    Clustering and the Moulton factor
    Serial correlation in DD models

 

READINGS

J.D. Angrist and J.S. Pischke, Mostly Harmless Econometrics: An Empiricists Companion, Princeton University Press, 2008.
Most of the readings are from MHE. Published journal articles should be available via JSTOR. Working papers are available from online sources.

I. REGRESSION RECAP

MHE, Chapters 1-3

The first two chapters explain our experimentalist perspective on applied econometrics. Chapter 3 covers
regression basics and more advanced topics related to regression and matching. We’ll skip everything
beyond a quick review of the basics. Know your regression anatomy and to be prepared to recite the
omitted variables bias formula when asked!

II. INSTRUMENTAL VARIABLES

Part 1

2SLS with constant effects; the Wald estimator, grouped data, two-sample IV
MHE, Section 4.1

J. Angrist and A. Krueger, “Instrumental Variables and the Search for Identification”, Journal of Economic Perspectives, Fall 2001.
J. Angrist, “Grouped Data Estimation and Testing in Simple Labor Supply Models”, Journal of Econometrics, February/March 1991.
J. Angrist, "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records", American Economic Review, June 1990.
J. Angrist and A. Krueger, “Split-Sample Instrumental Variables Estimates of the Returns to Schooling”, JBES, April 1995.
Inoue, Atsushi and G.Solon, “Two-Sample Instrumental Variables Estimators,” NBER Technical Working Paper 311, June 2005.

IV details

2SLS Mistakes: MHE, Section 4.6.1

The bias of 2SLS: MHE, Section 4.6.4

Part 2

Instrumental variables with heterogeneous potential outcomes
MHE, Section 4.4

G. Imbens and J. Angrist, “Identification and Estimation of Local Average Treatment Effects”, Econometrica, March 1994.
J. Angrist, G. Imbens, and D. Rubin, “Identification of Causal effects Using Instrumental Variables”, with comments and rejoinder, JASA, 1996.
J. Angrist and A. Krueger, "Does Compulsory Schooling Attendance Affect Schooling and Earnings?", Quarterly Journal of Economics 106, November 1991, 979-1014.
J. Angrist, “Instrumental Variables in Experimental Criminological Research: What, Why, and How”, Journal of Experimental Criminological Research 2, 2005, 1-22.

Models with variable and continuous treatment intensity
MHE, Section 4.5.3
J. Angrist and G. Imbens, “Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity”, JASA, June 1995.

Limited dependent variables
MHE, Section 4.6.3

J. Angrist, “Estimation of Limited Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice”, Journal of Business and Economic Statistics 19, January 2001.

III. DIFFERENCES-IN-DIFFERENCES AND PANEL DATA

MHE, Chapter 5
A. Abadie, A. Diamond, and J. Hainmueller, “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program”, Journal of the American Statistical Association, 2010 (forthcoming; available at ASA web site).

IV. REGRESSION-DISCONTINUITY DESIGNS

MHE, Chapter 6
T. Cook, “Waiting for Life to Arrive: A History of the Regression-Discontinuity Design in Psychology, Statistics, and Economics,” Journal of Econometrics 142 (2008), 636-654.
G. Imbens and T. Lemieux, “Regression Discontinuity Designs: A Guide to Practice”, Journal of Econometrics 142 (2008), 615-35.
D. Lee, “Randomized Experiments from Non-Random Selection in U.S. House Elections”, Journal of Econometrics 142, 2008.
J. Hahn, P. Todd, and W. van der Klaauw, “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design", Econometrica 69 (2001), 201-209.
J. Angrist and V. Lavy, “Using Maimonides Rule to Estimate the Effect of Class Size on Scholastic Achievement,” QJE, May 1999.
G. Imbens and K. Kalyanaraman, “Optimal Bandwidth Choice for the Regression Discontinuity Estimator,” NBER Working Paper No. 14726, February 2009.

V. NON-STANDARD STANDARD ERROR ISSUES

MHE, Section 3.1.3 (Review of asymptotic OLS inference)
MHE, Chapter 8
A. Chesher and I. Jewitt, “The Bias of a Heteroskedasticity-Consistent Covariance Matrix Estimator”, Econometrica 55, September 1987.
Moulton, Brent. 1986. "Random Group Effects and the Precision of Regression Estimates", Journal of Econometrics, 32, pp. 385-397.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan, "How Much Should We Trust Differences-in-Differences Estimates?", QJE 119 (February 2004), 249-275.
C. Cameron, J. Gelbach, and D. Miller, “Bootstrap-Based Improvements for Inference with Clustered
Errors”, The Review of Economics and Statistics, August 2008.